Orthogonal frequency division multiplexing (OFDM) has been widely adopted for a variety of wireless communication standards, such as WLAN IEEE 802.11a/g, DAB, TDMB, DVB-T, WiMax, and also 3GPP LTE. Due to its potential for low complexity receiver implementation, OFDM is particularly attractive for high-data rate transmission.
In OFDM, the transmission bandwidth is split into equidistantly spaced orthogonal sub-bands of identical width. Orthogonality is maintained under the prerequisite that the duration of the channel impulse response does not exceed the duration of the guard interval, and if the radio propagation channel conditions vary slowly enough. Both requirements are satisfied by proper selection of system parameters, such as subcarrier spacing and guard interval duration. Then transmission of one data symbol is described by the simple equationyk,l=hk,l·xk,l+nk,l  (1).
Here x is a transmitted symbol, h is a complex fading coefficient, n is a random noise sample, y is the corresponding received symbol, k is the OFDM subcarrier index, and/is the OFDM symbol index. The noise sample is characterized by the noise variance σn2. With different values for all different pairs of (k,l), this equation holds for all symbols in the time-frequency plane which is illustrated in FIG. 1. The above holds for a communication scheme with one transmit (Tx) antenna.
An OFDM communication scheme where multiple antennas are used both on the transmit side and the receive side is known as multiple-input multiple-output (MIMO) OFDM. In this case, each element in the time-frequency plane corresponds to the equationyk,l=Hk,l·xk,l+nk,l  (2),where x is a vector of transmitted symbol, H is a matrix of complex fading coefficients, n is a random noise sample vector, y is the corresponding received symbol vector. The random noise vector is characterized by its covariance matrix Φnm.
In a multi-user system, where transmission occurs from one transmitter to multiple receivers, regions in the time-frequency plane may be assigned to different users. The 3GPP LTE standard employs this kind of orthogonal frequency division multiple access (OFDMA) in the downlink, i.e. the transmission direction from a base station to a terminal. In LTE each element in the time-frequency plane is referred to as a resource element, and the entire time-frequency plane is divided into so-called resource blocks, which are rectangles of 12 subcarriers in frequency direction times 6 or 7 (depending on the cyclic prefix duration mode) OFDM symbols in time direction, as illustrated in FIG. 2.
As illustrated in FIG. 3, a resource block as a whole is assigned to a specific user.
Depending on the network load, resource blocks may also be unused, i.e., not allocated to any user.
The LTE standard describes a cellular network, where a supplied area is split into cells, each cell being equipped with a base station which serves the mobile stations in that cell. In LTE terminology a base station is referred to as an “evolved Node B” (eNB), and a mobile station or terminal is referred to as user equipment (UE). A simplified scheme of such a network is shown in FIG. 4. A communication terminal 1 is located in cell 4A near the border to an adjacent cell 4B. Terminal 1 is served by a base station 2. Terminal 1 will also receive signals transmitted by base station 3 of cell 4B that are, however, intended for another terminal (not shown) which is located in cell 4B.
In LTE, all cells of a network operate at the same center frequency, i.e., the frequency re-use factor is 1, which means that any mobile station will experience interference from neighboring cells in the network. The interference from a neighboring cell depends on the patterns of used and non-used resource blocks in the adjacent cells. To give an example, we assume, at a specific point in time, a resource block allocation as depicted in FIG. 3 to be valid in cell 4A, and a different resource block allocation, not shown, to be valid in the neighboring cell 4B. In the allocation scheme of FIG. 3 resource blocks with resource block index 5 and slot indices 5 and 6 are allocated to user 1 (terminal 1 in FIG. 4). In case these resource blocks in the frequency-time plane are concurrently used in cell 4B, i.e. allocated to another user (not shown), communication of terminal 1 when located near the border to cell 4B will be interfered from cell 4B. In case the same resource blocks are not allocated in cell 4B, there will be no interference on the communication of terminal 1 for signals in these resource blocks. From this example it will readily be understood that inter-cell interference varies in time and frequency. Particularly at the boundary between two cells, the level of received interference from the neighboring base station in those portions of the time-frequency plane where the interfering base station has resource blocks allocated to its own served users, will typically be significantly higher than the thermal noise of the receiver. Interference coordination between base stations of adjacent cells targets at keeping interference levels low.
Due to processing complexity constraints and limited bandwidth resources, when a network becomes more and more loaded with users, reception at a mobile station turns more and more from a noise limited operation to an interference limited operation. In addition, the communication channel towards an interfering base station is time variant and frequency selective. Thus, when a mobile station receives signals in an LTE network, the composite of noise and interference is generally varying both in time and frequency directions.
In a mobile radio receiver, in order to enable reliable data reception, a number of parameter estimation tasks need to be performed, e.g., time synchronization estimation, frequency synchronization estimation, channel estimation, interference level estimation, Doppler spread estimation, power delay profile estimation, feedback information estimation. Many existing OFDM receiver implementations are designed for a noise level which is constant across the employed frequency band. However, to achieve the highest data throughput in presence of time and frequency selective level of noise plus interference, the data demodulation and parameter estimation algorithms should take into account the characteristics of noise and interference.
Advanced error correcting codes are applied for reliable communication, e.g., LTE utilizes a so-called Turbo code. In a receiver the decoder is fed with softbits referred to as log likelihood ratio (LLR) values for optimum decoding results. For the example of a simple BPSK (Binary Phase Shift Keying) transmit symbol alphabet, comprising two symbols x ε{−1,1}, an LLR value is computed as
                    L        =                                            log              ⁡                              (                                  p                  ⁡                                      (                                                                  y                        |                        x                                            =                      1                                        )                                                  )                                                    log              ⁡                              (                                  p                  ⁡                                      (                                                                  y                        |                        x                                            =                                              -                        1                                                              )                                                  )                                              =                                    4              ·              Re                        ⁢                                          {                                                                            h                      *                                        ·                    y                                                        σ                    n                    2                                                  }                            .                                                          (        3        )            
Thus, computation of these LLR values requires knowledge about the noise level. If the noise level varies among received symbols of one codeword, respective noise levels for all symbols must be considered in the computation of respective LLR values, which are fed into the decoder. Equation (3) applies to the single-input-single output (SISO) case, i.e. where there is one transmit antenna and one receive antenna. In a case where signals from multiple receive antennas are available, the equation becomes
                    L        =                                            log              ⁡                              (                                  p                  ⁡                                      (                                                                  y                        |                        x                                            =                      1                                        )                                                  )                                                    log              ⁡                              (                                  p                  ⁡                                      (                                                                  y                        |                        x                                            =                                              -                        1                                                              )                                                  )                                              =                                    4              ·              Re                        ⁢                                          {                                                      H                    H                                    ·                                      Φ                    nn                                          -                      1                                                        ·                  y                                }                            .                                                          (        4        )            
The covariance matrix in this equation contains noise variances corresponding to all receive antennas on the main diagonal, and also the respective noise co-variances on the side diagonals. Assuming that all co-variances are zero, this equation describes what is known as maximum-ratio combining: Received symbols of all antennas are normalized by the respective noise variances and multiplied by corresponding channel amplitudes. When the side diagonals in the covariance matrix are non-zero, the equation describes optimum combining even in presence of noise correlation between receive antennas.
In a number of OFDM transmission schemes including LTE downlink, reference symbols are multiplexed into the time-frequency plane such as illustrated in FIG. 5. Reference symbols are data symbols which are known at the receiver and are used for parameter estimation tasks.
In an OFDM receiver, a number of parameter estimation tasks may be carried out using simple scalar products of vectors. For complex vectors a and b of length M, a scalar product is defined as
                    s        =                              ∑                          m              =              1                        M                    ⁢                                    a              m                        ·                                          b                m                *                            .                                                          (        5        )            
For example, when a and b are vectors of demodulated reference symbols from distinct OFDM symbols with a certain time gap in between, the angle of the scalar product s provides information on the residual frequency offset. Thus, this scalar product may serve as a frequency offset estimator used for frequency tracking. In practice, the values in the scalar product are all composed of an actual value plus a random noise term, am=aactual,m+anoise, bm=bactual,m+bnoise,m with anoise,m, bnoise,m being zero-mean Gaussian noise with respective variances σa,m2 and σb,m2. Then the scalar product based on noise-normalized vectors,
                              s          norm                =                              ∑                          m              =              1                        M                    ⁢                                                    a                m                            ·                              b                m                *                                                                    σ                                  a                  ,                  m                                            ·                              σ                                  b                  ,                  m                                                                                        (        6        )            offers the best possible consideration of respective noise variances, and thus, an optimum estimator. In addition, the absolute value of the computed scalar product provides a reliability figure on the estimation result, namely the angle.
LTE supports a number of link adaptation methods, in order to provide a certain quality of service (QoS) to mobile users. The modulation and coding scheme, i.e., the modulation alphabet (QPSK, 16-QAM, or 64-QAM) and the coding rate are adapted to the given link conditions, in order to meet a targeted maximum packet error rate. To meet that target, a mobile station sends a proposal for the choice of a modulation and coding scheme to the base station (channel quality indication—CQI). In addition there exists a MIMO transmission mode utilizing implicit beam-forming via precoding, which allows improved utilization of the spatial channel dimension. In this mode, the mobile station sends a proposal for the number of transmission layers (rank indication—RI) and best precoding matrix (precoding matrix index—PMI). The mobile station obtains all this feedback information based on an assessment of the channel conditions and sends its proposals to the base station. Typically, all this feedback information (FBI) is computed based on the signal-to-noise ratio which will be obtained after equalization.
A general concept of an OFDM receiver is e.g. described by J. Berkmann et al., “On 3G LTE Terminal Implementation—Standard, Algorithms, Complexities and Challenges”, International Wireless Communications and Mobile Computing Conference, Aug. 6-8, 2008, Crete Island, Greece.
Algorithms considering non-constant noise/interference levels have been described in literature, both for data demodulation and aspects of parameter estimation. To optimally cope with time and frequency selective interference, the receiver data demodulation, and also most parameter estimation tasks need to take into account the non-constant interference level. Demodulation and parameter estimation algorithms considering time/frequency-selective interference are typically more computationally intense than algorithms treating interference as a constant. Thus, when all parameter estimation algorithms individually take into account non-constant noise/interference, the added computational complexity to optimally consider non-constant rather than constant noise/interference is significant.
The object of the invention, therefore, is to provide an OFDM baseband receiver architecture which supports frequency selective noise estimation, and especially frequency selective soft-metric weighting for interference limited environments. A more general object of the invention is to suggest an OFDM baseband receiver architecture and reception method with improved estimation performance and reduced computational complexity which directly translates into reduced costs, both in terms of manufacturing and power consumption.